Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/20/2026 has been entered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,227,197. Although the claims at issue are not identical, they are not patentably distinct from each other.
Independent claim 1 of the instant application is directed to an information processing apparatus that controls a vehicle, the information processing apparatus including circuitry configured to determine whether to control the vehicle to decelerate based on statistical information related to the driver’s action in the driver’s normal state, and control the vehicle to decelerate and to enter a deceleration operation mode in a case that the circuitry determines to control the vehicle to decelerate.
Similarly, claim 13 of U.S. Patent No. 12,227,197 is directed to an information processing apparatus that controls a vehicle, the information processing apparatus including circuitry configured to determine, when operating in a manual operation mode, whether to cause the vehicle to decelerate based on a state of a driver of the vehicle, and, based on the determining indicating to cause the vehicle to decelerate, cause the vehicle to decelerate and enter a deceleration operation mode. Claim 13 of U.S. Patent No. 12,227,197 further recites outputting audio and visual notifications, causing the vehicle to cease decelerating in response to a driver input device, and storing undeletable records regarding operation of the input device.
The difference between claim 1 of the instant application and claim 13 of U.S. Patent No. 12,227,197 is that claim 1 of the instant application recites determining whether to control the vehicle to decelerate based on statistical information related to the driver’s action in the driver’s normal state, whereas claim 13 of U.S. Patent No. 12,227,197 recites determining whether to cause the vehicle to decelerate based on a state of the driver. However, this difference does not render the claims patentably distinct. Determining the state of the driver based on statistical information related to the driver’s actions in the driver’s normal state would have been an obvious way to implement the determination of the driver state recited in claim 13 of U.S. Patent No. 12,227,197. The common disclosure describes recognizing statistical stable actions peculiar to the driver in a normal awakening state, determining deviation of actions due to lowering of awakening, monitoring sight line statistical behavior stability, and using such driver-state information to determine whether vehicle deceleration or emergency stop control should be performed. Thus, using statistical information related to the driver’s normal-state actions to determine whether to decelerate is merely an obvious implementation of determining whether to decelerate based on the state of the driver.
Accordingly, claim 1 of the instant application is an obvious variation of claim 13 of U.S. Patent No. 12,227,197 and is not patentably distinct therefrom.
The remaining claims 2-20 of the instant application are likewise not patentably distinct from claims 1-20 of U.S. Patent No. 12,227,197. The dependent claims of the instant application merely further define known aspects of the same driver-state-based vehicle deceleration system, including statistical driver behavior information, sight line information, awakening-state determination, notification to persons inside the vehicle, cancellation of deceleration by the driver, recording of cancellation information, remote communication, and corresponding method/system implementations. These features are either expressly recited in claims 1-20 of U.S. Patent No. 12,227,197 or would have been obvious variations of the patented claims in view of the common disclosure.
Therefore, claims 1-20 of the instant application are rejected on the ground of nonstatutory double patenting over claims 1-20 of U.S. Patent No. 12,227,197.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites “the notification is presented by a light that emits a predetermined color based on the awakening state of the driver.” However, claim 10 depends from claim 7, and claim 7 does not recite or otherwise provide antecedent basis for “the notification.” Therefore, it is unclear what notification is being referenced.
Claim 19 recites “An information processing system that controls a vehicle,” but the body of the claim recites “the information processing apparatus comprising.” Thus, the claim is internally inconsistent because it is unclear whether the claimed subject matter is directed to an information processing system or an information processing apparatus.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-12, 15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Fung et al. (US 2012/0212353 A1) in view of Wu et al. (US 2012/0169503 A1).
Regarding claim 1, Fung teaches an information processing apparatus that controls a vehicle, including ECU 150/response system 199 configured to receive monitoring information, determine a driver state/body state index, and modify control of vehicle systems based on driver drowsiness (paras 0134-0137, 0150-0155). Fung further teaches that the response system may be implemented by ECU 150 and may control vehicle systems of the motor vehicle (paras 0091-0092, 0107-0108).
Fung teaches circuitry configured to control the vehicle to decelerate and to enter a deceleration operation mode in a case that the circuitry determines to control the vehicle to decelerate, because Fung teaches automatically reducing vehicle speed in response to driver drowsiness. In particular, Fung teaches receiving drowsiness information, determining whether the driver is drowsy, determining whether cruise control is operating, determining the current cruising speed, and reducing the cruising speed by a predetermined percentage or amount, such as reducing from 60 mph to 30 mph, so that the system “may automatically reduce the speed of motor vehicle 100” (paras 0258-0259). Fung also teaches ramping down automatic cruise control and slowing the vehicle gradually to a predetermined speed when the body state index indicates the auto cruise control status should be off (paras 0255-0256).
Fung does not expressly teach that the determination to decelerate is based on statistical information related to the driver’s action in the driver’s normal state.
Wu teaches determining driver impairment using such statistical information. Wu teaches sensing the position of the driver’s head at a plurality of time points, determining changes in the driver’s head position, evaluating whether the changes exhibit periodic or quasi-periodic patterns, and determining whether the driver is impaired based on the pattern of changes in the driver’s head (Abstract). Wu further teaches that statistical signal processing algorithms are applied in time and frequency domains to acquired driver head-motion data to determine whether the driver is drowsy, and that a non-drowsy/unimpaired driver does not show a regular pattern of head motion while a fatigued driver shows head-motion patterns such as nods (paras 0032-0034). Wu further teaches using statistical metrics including variance, standard deviation, dispersion coefficient, and auto-correlation function to distinguish different driver head-motion signals, including regular normal driving, random normal driving, drowsy driving, and normal-drowsy driving (paras 0040-0043).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to modify Fung’s driver-behavior response system to use Wu’s statistical driver head-motion information, including statistical information obtained from normal driving and drowsy driving conditions, when determining whether the driver is drowsy and whether to reduce/decelerate the vehicle. One of ordinary skill in the art would have been motivated to do so because Fung seeks to determine driver drowsiness/body state so that vehicle systems can be automatically modified to reduce risks posed by a drowsy driver, and Wu provides a known statistical signal-processing technique for determining driver impairment based on driver actions/head motion. The combination would have predictably improved Fung’s driver-state determination by using statistical driver-action information to more reliably identify drowsiness before automatically reducing vehicle speed.
Regarding claim 2, Fung in view of Wu teaches the information processing apparatus of claim 1, as set forth above.
Wu further teaches wherein the statistical information includes statistical behavior stability related to a behavior of the driver. In particular, Wu teaches applying “statistical signal processing algorithms” to acquired driver head-motion data to analyze patterns of head motion and determine whether the driver is drowsy. Wu teaches that an unimpaired/non-drowsy driver “does not show a regular pattern of head motions,” while a fatigued driver shows head-motion patterns such as nods, and that the statistical signal processing analysis is used to analyze and judge the driver’s state and degree of fatigue or impairment (paras 0032-0034).
Wu also teaches using statistical metrics such as variance, standard deviation, dispersion coefficient, and auto-correlation function to extract characteristic values of random signals, where the characteristic values distinguish between different driver head-motion signals, including regular normal driving, random normal driving, drowsy driving, and normal-drowsy driving (paras 0040-0043).
Thus, Wu’s statistical evaluation of driver head-motion behavior, including detecting the regularity/irregularity, variance, dispersion, and auto-correlation of the driver’s head-motion pattern, teaches statistical behavior stability related to a behavior of the driver
Regarding claim 3, Fung teaches monitoring the driver’s eyes to determine the driver state. In particular, Fung teaches that ECU 150 receives information from optical sensing device 162, such as a video camera, and analyzes images of the driver’s eyes to determine whether the driver is in a normal state or a drowsy state (paras 0161-0162). Fung further teaches receiving optical/thermal information, analyzing eyelid movement, and determining a body state index, and also teaches that “the movement of the eyeballs could also be analyzed” (paras 0164-0168).
Wu teaches applying statistical signal processing to driver-motion data to determine driver drowsiness/impairment. In particular, Wu teaches that statistical signal processing algorithms are applied in time and frequency domains to acquired driver-motion data to analyze patterns and determine whether the driver is drowsy (paras 0032-0034). Wu further teaches using statistical metrics including variance, standard deviation, dispersion coefficient, and auto-correlation function to distinguish signals corresponding to normal driving and drowsy driving (paras 0040-0043).
Therefore, it would have been obvious to apply Wu’s statistical signal-processing analysis to Fung’s eye/eyeball movement information to determine driver drowsiness/body state, because Fung already teaches analyzing eye/eyeball movement to determine driver state, and Wu teaches that statistical signal processing of driver-motion behavior improves detection of driver impairment.
Regarding claim 4, Fung teaches monitoring the eyes of the driver to determine the state of the driver. In particular, Fung teaches that ECU 150 receives information from an optical sensing device, such as a video camera, and analyzes images of the driver’s eyes to determine whether the driver is in a normal state or a drowsy state. Fung also teaches that the “movement of the eyeballs could also be analyzed” and that the system may analyze eye movement over an interval of time and look at average eye movements (paras 0161-0168).
Wu teaches applying statistical signal processing to driver-motion data to determine driver impairment. In particular, Wu teaches applying “statistical signal processing algorithms” in time and frequency domains to acquired driver-motion data, and determining driver impairment based on regularity/irregularity of driver motion patterns. Wu further teaches using statistical metrics, including variance, standard deviation, dispersion coefficient, and auto-correlation function, to distinguish between normal driving and drowsy driving states (paras 0032-0043).
Therefore, it would have been obvious to apply Wu’s statistical signal-processing analysis to Fung’s monitored eye/eyeball movement information in order to statistically evaluate the stability of the driver’s eye/sight-line movement over time. Such statistical evaluation of eye/eyeball movement over time reasonably corresponds to statistical stability of sight line relation to saccade of the driver, because saccades are rapid eye movements, and Fung already teaches analyzing eye/eyeball movement while Wu teaches statistical evaluation of driver movement patterns to determine drowsiness.
Regarding claim 6, Fung teaches determining a driver body state index based on monitored driver behavior, including head movement, head distance, and/or head motion over time. Fung further teaches that the alert state of the driver may be associated with a predetermined distance between the driver’s head and the headrest, and that the predetermined distance may be “a factory set value or a value determined by monitoring a driver over time.” Fung also teaches increasing the body state index when the driver’s head moves closer to or farther from the headrest relative to the predetermined distance, thereby determining drowsiness based on a learned or monitored driver reference value (paras 0179-0181).
Wu teaches determining driver impairment using statistical driver head-motion information and thresholds. In particular, Wu teaches comparing normal driving and drowsy driving conditions using statistical metrics such as dispersion coefficient, variance, and auto-correlation, and discusses determining a “specific threshold value and range” based on differences between regular normal driving and drowsy driving conditions (paras 0052-0054). Wu further teaches that “appropriate thresholds are determined” from the statistical head-motion data and used to automatically detect periodic head motions indicative of a drowsy driving state (paras 0065-0070).
Therefore, it would have been obvious to configure Fung’s response system to obtain, by monitoring learning, a driver-specific threshold for determining lowering of the driver’s consciousness state, as taught by Wu. One of ordinary skill in the art would have been motivated to do so because Fung already determines driver body state based on monitored driver behavior over time and uses the driver state to modify vehicle control, while Wu teaches using statistical analysis of normal and drowsy driver motion to determine thresholds for detecting driver drowsiness. Using a monitored driver-specific threshold would have predictably improved Fung’s driver-state determination by accounting for the driver’s own normal behavior before determining that the driver’s consciousness/alertness has lowered.
Regarding claim 7, Fung teaches determining a driver state/body state index corresponding to the driver’s drowsiness or alertness. In particular, Fung teaches that response system 199 receives monitoring information, determines the driver state, and determines whether the driver is drowsy. Fung further teaches that the driver state may be “normal or drowsy” or may include three or more states between normal and very drowsy/asleep (paras 0134-0137). Fung also teaches determining a “body state index” that measures the drowsiness of the driver and ranges from alertness to drowsiness/asleep (paras 0152-0153).
Wu teaches determining driver impairment/drowsiness based on statistical driver-motion information. In particular, Wu teaches applying “statistical signal processing algorithms” to driver head-motion data to analyze patterns of head motion and determine whether the driver is drowsy, and teaches that a non-drowsy driver does not show a regular pattern of head motion while a fatigued driver shows head-motion patterns such as nods (paras 0032-0034). Wu further teaches using statistical metrics including variance, standard deviation, dispersion coefficient, and auto-correlation function to distinguish different driver-motion signals, including normal driving and drowsy driving (paras 0040-0043).
Therefore, it would have been obvious to use Wu’s statistical driver-motion information in Fung’s driver-state/body-state-index determination to determine the driver’s awakening state. One of ordinary skill in the art would have been motivated to do so because Fung already determines the driver’s alert/drowsy state to modify vehicle control, and Wu provides a known statistical technique for determining the driver’s fatigue or impairment state from driver behavior.
Regarding claim 8, Fung teaches that the circuitry is further configured to output a notification based on a determination that the awakening state has lowered, the notification being presented to a person inside the vehicle. In particular, Fung teaches that response system 199 receives drowsiness information and determines whether the driver is drowsy. Once the response system detects the drowsy state, it may activate feedback mechanisms inside the vehicle, including tactile devices, lights or visual indicators, and sounds through speakers. Fung teaches that a warning may be displayed on a display screen, overhead lights or other visual indicators may be turned on, and sounds may be generated through speakers, including spoken words or alarms (paras 0209-0212).
Wu further teaches detecting driver impairment/drowsiness using statistical driver-motion information, including statistical signal processing of driver head-motion data, as discussed above with respect to claim 1.
Therefore, it would have been obvious to configure Fung’s vehicle control system, as modified by Wu’s statistical driver-state determination, to output a notification inside the vehicle based on the determined lowered awakening/drowsy state. One of ordinary skill in the art would have been motivated to do so because Fung expressly uses internal visual and audible feedback to alert the driver when drowsiness is detected, and such notifications improve safety by attempting to wake or alert a drowsy vehicle occupant before or during vehicle-system control.
Regarding claim 9, Fung teaches wherein the person inside the vehicle is not the driver. In particular, Fung teaches that when the response system detects that the driver is drowsy, the system may activate feedback mechanisms inside the vehicle, including lights or other visual indicators and sounds through speakers. Fung teaches that overhead lights or other visual indicators may be turned on and that various sounds may be generated through speakers inside the vehicle (paras 0209-0212). Because these audio and visual notifications are output inside the vehicle cabin, they would be presented to persons inside the vehicle, including occupants/passengers who are not the driver. Fung also recognizes that drowsy driving can create potential harm to “other vehicle occupants,” thereby providing reason to notify occupants inside the vehicle of the driver’s drowsy condition (para 0002).
Therefore, it would have been obvious to output Fung’s in-cabin audio/visual notification such that it is presented to a non-driver occupant/passenger inside the vehicle, because Fung’s system already outputs cabin notifications in response to driver drowsiness and recognizes safety risks to other vehicle occupants.
Regarding claim 10, Fung teaches outputting a visual notification based on the driver’s drowsiness/awakening state. In particular, Fung teaches that, when the response system detects that the driver is drowsy, the response system may activate one or more feedback mechanisms inside the vehicle, including lights or other visual indicators. Fung further teaches that a warning may be displayed on a display screen, overhead lights or other visual indicators may be turned on, and sounds may be generated through speakers. Fung also teaches that visual devices are modified according to the level of drowsiness and that the brightness of lights activated by visual devices when a driver is drowsy may be varied in proportion to the level of drowsiness.
Wu further teaches alerting a driver of drowsiness by making a sound or flashing a light when periodic head motions indicative of a drowsy driving state are detected.
Therefore, it would have been obvious to present Fung’s visual notification by a light that emits a predetermined color based on the awakening/drowsiness state of the driver. Fung already teaches activating lights/visual indicators and varying visual output based on the driver’s level of drowsiness, and Wu teaches using a flashing light to alert a drowsy driver. Selecting a predetermined color for the light corresponding to the driver’s awakening/drowsiness state would have been an obvious design choice for visually communicating the driver-state notification to vehicle occupants, because color-coded lights were a known and predictable way to convey warning levels or states.
Regarding claim 11, Fung teaches outputting audible warnings inside the vehicle in connection with driver drowsiness and vehicle braking/control. In particular, Fung teaches that, when a driver is drowsy, the response system may generate sounds through speakers, including spoken words, music, alarms, or other sounds to alert the driver (paras 0209-0212). Fung further teaches a collision mitigation braking system that, when the driver is drowsy, provides a forward collision warning as a visual alert and/or audible alert, and may also apply light braking to slow the vehicle (paras 0290-0292). Fung further teaches using visual and/or audible warnings and applying braking in a second warning stage of the collision mitigation braking system (Fig. 76).Wu further teaches detecting driver impairment/drowsiness using statistical driver-motion information, as discussed above with respect to claim 1.
Therefore, it would have been obvious to configure Fung’s system, as modified by Wu’s statistical driver-state determination, to output an audible announcement inside the vehicle after initiating vehicle deceleration/braking in response to the detected drowsy driver condition. One of ordinary skill in the art would have been motivated to do so because Fung already teaches audible in-cabin warnings and braking/vehicle-speed control in response to driver drowsiness, and providing an audible warning during or after initiation of braking would inform vehicle occupants of the vehicle-control condition and improve occupant preparedness during the braking/deceleration event.
Regarding claim 12, Fung teaches wherein the circuitry is further configured to monitor the state of the driver. In particular, Fung teaches response system 199/ECU 150 receiving monitoring information, determining the driver state, and determining whether the driver is drowsy (paras 0134-0137). Fung also teaches monitoring the driver using optical sensing devices, thermal sensing devices, proximity sensors, and other monitoring systems to determine a body state index corresponding to the driver’s drowsiness/alertness (paras 0096-0099, 0152-0155).
Wu also teaches monitoring the driver by sensing the position of the driver’s head at a plurality of time points, determining changes in the head position, and determining whether the driver is impaired based on the pattern of head-position changes.
Regarding claim 16, Fung teaches determining a driver state/body state index corresponding to the driver’s drowsiness or alertness. In particular, Fung teaches that response system 199 may determine a level of drowsiness for the driver, where the level of drowsiness may indicate states such as “not drowsy,” “slightly drowsy,” “drowsy,” “very drowsy,” and “extremely drowsy.” Fung further teaches that the level of drowsiness may be associated with a body state index, and that the response system receives monitoring information, determines whether the driver is drowsy, determines the level of drowsiness, and modifies vehicle control based on the level of drowsiness (paras 0144-0148).
Wu teaches communicating monitored driver-status information to a remote location. In particular, Wu teaches that collected driver-monitoring data may be transmitted by wire or wirelessly to a computing system, and that the data could be transmitted to a “remote location for analysis and monitoring.” Wu further teaches that the computing system may include communication mechanisms for transmitting signals to the driver, vehicle systems, and/or a remote location (para 0072).
Therefore, it would have been obvious to configure Fung’s response system, as modified by Wu, to communicate with a remote operation/monitoring system and send the awakening status/body state/drowsiness status of the driver to the remote system. One of ordinary skill in the art would have been motivated to do so because Fung determines the driver’s drowsiness/body state for vehicle-control purposes, and Wu teaches transmitting driver-monitoring information to a remote location for analysis and monitoring. Sending the determined driver awakening/drowsiness status to a remote system would have predictably allowed remote monitoring of the driver’s condition and improved vehicle safety oversight.
Regarding claim 18, Fung teaches an information processing method for controlling a vehicle. In particular, Fung teaches response system 199/ECU 150 receiving monitoring information, determining a driver state/body state index, and modifying control of vehicle systems based on driver drowsiness (paras 0134-0137, 0150-0155). Fung further teaches controlling vehicle systems of a motor vehicle using ECU 150 (paras 0091-0092, 0107-0108).
Fung teaches controlling the vehicle to decelerate and to enter a deceleration operation mode in a case that it is determined to control the vehicle to decelerate, because Fung teaches automatically reducing vehicle speed in response to driver drowsiness. In particular, Fung teaches receiving drowsiness information, determining whether the driver is drowsy, determining whether cruise control is operating, determining the current cruising speed, and reducing the cruising speed by a predetermined percentage or amount, such as reducing from 60 mph to 30 mph, so that the system “may automatically reduce the speed of motor vehicle 100” (paras 0258-0259). Fung also teaches ramping down automatic cruise control and slowing the vehicle gradually to a predetermined speed when the body state index indicates that automatic cruise control should be turned off (paras 0255-0256).
Fung does not expressly teach that the determination to decelerate is based on statistical information related to the driver’s action in the driver’s normal state.
Wu teaches determining driver impairment using statistical information related to driver action in a normal state. Wu teaches sensing the position of the driver’s head at a plurality of time points, determining changes in the driver’s head position, evaluating whether the changes exhibit periodic or quasi-periodic patterns, and determining whether the driver is impaired based on the pattern of changes in the driver’s head. Wu further teaches applying statistical signal processing algorithms in time and frequency domains to acquired driver head-motion data to determine whether the driver is drowsy, and teaches that a non-drowsy/unimpaired driver does not show a regular pattern of head motion while a fatigued driver shows head-motion patterns such as nods. Wu also teaches using statistical metrics including variance, standard deviation, dispersion coefficient, and auto-correlation function to distinguish normal driving and drowsy driving conditions.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to modify Fung’s driver-behavior response method to use Wu’s statistical driver head-motion information, including statistical information from normal and drowsy driver actions, when determining whether the driver is drowsy and whether to reduce/decelerate the vehicle. One of ordinary skill in the art would have been motivated to do so because Fung seeks to determine driver drowsiness/body state so that vehicle systems can be automatically modified to reduce risks posed by a drowsy driver, and Wu provides a known statistical signal-processing technique for determining driver impairment based on driver actions/head motion. The combination would have predictably improved Fung’s driver-state determination by using statistical driver-action information to more reliably identify drowsiness before automatically reducing vehicle speed.
Regarding claim 19, Fung teaches an information processing system that controls a vehicle, including response system 199/ECU 150 configured to receive driver monitoring information, determine a driver state/body state index, and modify control of vehicle systems based on driver drowsiness (paras 0134-0137, 0150-0155). Fung further teaches monitoring devices configured to monitor the driver, including optical sensing device 162, thermal sensing device 163, proximity sensor 134, and bio-monitoring sensor 164 (paras 0096-0099). Fung also teaches receiving monitoring information from monitoring systems and determining a body state index of the driver, which measures driver drowsiness (paras 0151-0153).
Fung teaches circuitry configured to control the vehicle to decelerate and to enter a deceleration operation mode in a case that the circuitry determines to control the vehicle to decelerate. In particular, Fung teaches receiving drowsiness information, determining whether the driver is drowsy, determining whether cruise control is operating, determining the current cruising speed, and reducing the cruising speed by a predetermined percentage or amount. Fung expressly teaches that the system “may automatically reduce the speed of motor vehicle 100” because slowing the vehicle may reduce risks posed by a drowsy driver (paras 0258-0259). Fung also teaches ramping down automatic cruise control and slowing the vehicle gradually to a predetermined speed based on the body state index (paras 0255-0256).
Fung does not expressly teach determining whether to control the vehicle to decelerate based on statistical information related to the driver’s action in the driver’s normal state.
Wu teaches determining driver impairment using statistical information related to driver action in a normal state. Wu teaches sensing the position of the driver’s head at a plurality of time points, determining changes in the driver’s head position, evaluating whether the changes exhibit periodic or quasi-periodic patterns, and determining whether the driver is impaired based on the pattern of changes in the driver’s head. Wu further teaches that statistical signal processing is used to analyze and judge a driver’s state and degree of fatigue or impairment, and that a non-drowsy/unimpaired driver does not show a regular pattern of head motion while a fatigued driver shows head-motion patterns such as nods (paras 0033-0034). Wu also teaches using statistical metrics including variance, standard deviation, dispersion coefficient, and auto-correlation function to distinguish normal driving and drowsy driving conditions (paras 0040-0043).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to modify Fung’s information processing system to use Wu’s statistical driver head-motion information, including statistical information from normal and drowsy driver actions, when determining whether the driver is drowsy and whether to reduce/decelerate the vehicle. One of ordinary skill in the art would have been motivated to do so because Fung seeks to determine driver drowsiness/body state so that vehicle systems can be automatically modified to reduce risks posed by a drowsy driver, and Wu provides a known statistical signal-processing technique for determining driver impairment based on driver actions/head motion. The combination would have predictably improved Fung’s driver-state determination by using statistical driver-action information to more reliably identify drowsiness before automatically reducing vehicle speed.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Fung in view of Wu, and further in view of Smith (US 2010/0007479 A1).
Regarding claim 5, Fung in view of Wu teaches the information processing apparatus of claim 1, as set forth above.
Fung teaches determining a driver state/body state index and modifying vehicle control based on the driver state, including collision-warning/braking control and vehicle-speed reduction when the driver is drowsy.
Wu teaches using statistical driver-behavior information to determine driver impairment/drowsiness, including statistical signal processing of driver motion data in normal and drowsy driving conditions.
Fung and Wu do not expressly teach determining whether to control the vehicle to decelerate based on a statistical tendency of coincidence between a direction of a sight line of the driver and a recognized position of an object ahead of the vehicle.
Smith teaches recognizing a position of an object ahead of the vehicle and evaluating the driver’s gaze direction relative to the forward object/event. In particular, Smith teaches a forward collision sensor configured to detect objects forward of the host vehicle and supply azimuth and range data to an adaptive warning controller. Smith further teaches a driver state sensor that detects the orientation of the driver’s head or eyes to infer the driver eye gaze direction, including whether the driver eye gaze is forward or non-forward. Smith also teaches that, after a precipitating forward collision event, the controller keeps track of elapsed steady-state time and the duration of continuous non-forward driver gaze, and compares the elapsed steady-state time with the continuous non-forward gaze time to infer whether the driver is aware of the situation.
Therefore, it would have been obvious to modify Fung’s driver-behavior vehicle-control system, as modified by Wu’s statistical driver-behavior analysis, to further use Smith’s comparison of driver gaze direction relative to a detected forward object/event when determining whether to modify vehicle control, including vehicle deceleration. One of ordinary skill in the art would have been motivated to do so because Fung already modifies warning/braking/vehicle-speed control based on driver drowsiness and collision-related conditions, Wu teaches statistical analysis of driver behavior to determine impairment, and Smith teaches that the relationship between driver gaze direction and a detected forward object/event is useful for determining driver awareness of the hazardous situation. Using Smith’s gaze/object relationship would have predictably improved Fung’s system by allowing the vehicle-control determination to account for whether the driver’s sight line indicates awareness of an object ahead before initiating or modifying warning/deceleration control.
Claim 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Fung in view of Wu, and further in view of Johnson et al. (US 2015/0329091 A1).
Regarding claim 13, Fung in view of Wu teaches the information processing apparatus of claim 1, as set forth above.
Fung teaches controlling the vehicle to decelerate based on a driver state/body state, including automatically reducing vehicle speed when the driver is drowsy.
Wu teaches determining driver impairment/drowsiness based on statistical driver-behavior information, as discussed above with respect to claim 1.
Fung and Wu do not expressly teach determining whether a cancel instruction is received from the driver after starting the deceleration operation mode and canceling the deceleration based on the cancel instruction.
Johnson teaches a braking control system that controls automatic braking after the brake system has already been actuated. In particular, Johnson teaches that, after an automatic emergency braking system is triggered and after the brakes are initially applied, the braking control system determines vehicle conditions and may apply or release the vehicle brakes based on such determinations. Johnson further teaches that the braking control system may have driver overrides, where the system “stops applying the vehicle brakes” when it is determined that the driver has taken over control of the vehicle, such as when the driver is applying the brakes, applying the accelerator, or evasively maneuvering the vehicle to avoid a collision (paras 0017, 0022, 0024).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to modify Fung’s vehicle deceleration control system, as modified by Wu’s statistical driver-state determination, to include Johnson’s driver override/cancel control after automatic deceleration or braking has begun. One of ordinary skill in the art would have been motivated to do so because Fung automatically modifies vehicle speed/control based on driver drowsiness, Wu improves the determination of driver impairment using statistical driver-behavior information, and Johnson teaches that, after automatic braking has been actuated, the system should allow an unimpaired driver to override the automatic braking and release the brakes when the driver has taken over control. The combination would have predictably improved driver control and safety by allowing cancellation of automatic deceleration when the driver affirmatively takes over control of the vehicle.
Regarding claim 14, Johnson teaches determining a driver override after automatic braking has been actuated and releasing the brakes based on the driver override. In particular, Johnson teaches that the braking control system controls automatic braking after the brakes are initially applied, determines whether the driver is impaired, determines whether there is a driver override, and releases the brakes when there is a driver override (paras 0017-0024).
Fung teaches receiving user input by way of an input device/switch. In particular, Fung teaches that user input device 111 may be “one or more buttons, switches, a touch screen, touchpad, dial, pointer or any other type of input device,” and that input device 111 may be an “ON/OFF switch” (para 0104). Fung further teaches that a user may switch input device 111 to the OFF position, which turns off body state monitoring and prevents response system 199 from modifying control of vehicle systems (para 0138).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to configure the driver override/cancel instruction of Johnson, as applied to Fung’s vehicle-control system, to be received via operation of a switch as taught by Fung. One of ordinary skill in the art would have been motivated to do so because Fung already teaches a switch-type user input device for preventing automatic modification of vehicle systems, and Johnson teaches permitting a driver override after automatic braking has begun. Using a switch as the driver-operated invalidation input would have been a predictable and simple implementation for allowing the driver to cancel automatic deceleration control.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Fung in view of Wu, further in view of Johnson, and further in view of Doyle (US 2004/0039503 A1).
Regarding claim 15, Fung in view of Wu and Johnson teaches the information processing apparatus of claim 13, as set forth above.
Johnson teaches determining a driver override after automatic braking has been actuated and releasing the brakes based on the driver override. In particular, Johnson teaches that, after an automatic braking event is triggered and after the brakes are initially applied, the braking control system determines whether there is a driver override, such as whether the driver is applying the brakes, applying the accelerator, or evasively maneuvering the vehicle, and releases the brakes when there is a driver override (paras 0017, 0022, 0024).
Doyle teaches recording vehicle event data in a secure recording unit in an undeletable/tamper-proof manner. In particular, Doyle teaches a secure event data recording system including an event data recorder, a memory device configured to store event data processed in the event data recorder, and a tamper proof sealing mechanism that bars access to the memory device, event data recorder, and input/output port without causing an irreparable breach of the tamper proof sealing mechanism (paras 0011-0013). Doyle further teaches that the event data recorder records vehicle performance data such as speed, acceleration, impact force, date, and time, and records vehicular event data to memory in either an overwrite mode or a non-overwrite mode (paras 0018, 0022). Doyle also teaches a read-only output port through which event data stored in memory can be read without allowing the data to be manipulated in place within the memory device (para 0024).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to modify the driver override/cancel control of Fung, Wu, and Johnson to record the cancel instruction/driver override event in a secure recording unit as taught by Doyle. One of ordinary skill in the art would have been motivated to do so because Johnson’s driver override occurs during an automatic braking/deceleration event and directly affects release of the vehicle brakes, and Doyle teaches securely recording vehicle event/performance data in a tamper-proof/non-overwrite manner so that the recorded data can be later verified and used to determine the cause of a vehicle event. Recording the driver override/cancel instruction in such a secure recording unit would have predictably preserved evidence of why the automatic deceleration was canceled and improved post-event verification of vehicle operation.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Fung in view of Wu, and further in view of Yopp (US 2015/0066284 A1).
Regarding claim 17, Fung in view of Wu teaches the information processing apparatus of claim 16, as set forth above.
Fung teaches determining a driver state/body state and modifying vehicle control based on the driver state, including automatically reducing vehicle speed when the driver is drowsy.
Wu teaches communicating driver-monitoring information to a remote location for analysis and monitoring, as discussed above with respect to claim 16.
Fung and Wu do not expressly teach receiving a vehicle-control instruction from the remote-control operation system.
Yopp teaches a vehicle computer configured to communicate with a remote server and receive instructions for vehicle control based on an impaired driver condition. In particular, Yopp teaches that vehicle computer 105 includes autonomous driving module 106 for autonomously operating the vehicle, including in response to instructions received from server 125. Yopp further teaches that server 125 may determine what action should be taken after an impaired operator state is reported and provide direction to vehicle computer 105, such as directing the vehicle to pull over, drive autonomously to an emergency health care facility, or proceed to a rendezvous with an emergency provider (paras 0004, 0006, 0010). Yopp also teaches that, once assistance is requested, vehicle computer 105 receives instructions from the server, call center, or other queried entity concerning operations to be performed by autonomous driving module 106, including driving route and other driving instructions (para 0023).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to modify Fung’s driver-state based vehicle-control system, as modified by Wu’s remote monitoring/communication of driver status, to receive vehicle-control instructions from a remote system as taught by Yopp. One of ordinary skill in the art would have been motivated to do so because Fung controls the vehicle in response to driver drowsiness, Wu teaches transmitting driver-monitoring information to a remote location for analysis and monitoring, and Yopp teaches using a remote server or assistance entity to provide vehicle-control instructions when an impaired driver condition is detected. The combination would have predictably improved safety by allowing a remote system to provide appropriate vehicle-control instructions, such as pulling over, proceeding to a safe location, or otherwise controlling vehicle operation, after receiving the driver’s awakening/drowsiness status.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kobana et al (US 2013/0018549) Fig. 1 and abstract
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/OMEED ALIZADA/Primary Examiner, Art Unit 2686
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